KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision

Proceedings of the 28th International Conference on Computational Linguistics, pages 1544–1550 Barcelona, Spain (Online), December 8-13, 2020 1544 KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision Xinyu Zuo1,2, Yubo Chen1,2, Kang Liu1,2, Jun Zhao1,2 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China 2School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China {xinyu.zuo,yubo.chen,kliu,jzhao}@nlpr.ia.ac.cn Abstract Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions and limited in size, which makes supervised methods hard to detect causal relations between events. To solve this data lacking problem, we investigate a data augmentation framework for ECD, dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis). Experimental results on two benchmark datasets EventStoryLine corpus and Causal- TimeBank show that 1) KnowDis can augment available training data assisted with the lexical and causal commonsense knowledge for ECD via distant supervision, and 2) our method outperforms previous methods by a large margin assisted with automatically labeled training data. 1 Introduction Event causality detection (ECD) aims to identify causal relations between events from texts, which may provide crucial clues for many NLP tasks, such as information extraction, logical reasoning, question answering, and others (Girju, 2003; Oh et al., 2013; Oh et al., 2017). For example, the causal relation that Kimani Gray was killed because of a police attack is needed to be detected in the following sentence: ”Kimani Gray, a young man who likes football, was killed in a police attack shortly after a tight match.” This task is usually modeled as a classification problem, i.e. determining whether there is a causal relation between two events in a sentence. To this end, most existing methods adopt a supervised learning paradigm (Mirza and Tonelli, 2016; Riaz and Girju, 2014; Hashimoto et al., 2014; Hu and Walker, 2017; Gao et al., 2019; Zuo et al., 2020). Although these methods have achieved good performance, they usually need large-scale annotated training data. However, existing event causality detection datasets are relatively small. For example, the EventStoryLine Corpus (Caselli and Vossen, 2017) only contains 258 documents, 4316 sentences, and 1770 causal event pairs. These small datasets are in low coverage of causal expressions and obstacle NLP applications deployed on large-scale data. Recent improvements of distant supervision have been proven to be effective to label training data for some tasks, such as relation extraction (Mintz et al., 2009), event detection (Chen et al., 2017), and so on. Therefore, we investigate a distant data augmentation framework for solving the data lacking problem on the ECD task, dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis), to automatically label available data. We argue that a sentence contains an event pair with a high probability of causality and expresses its causal semantic can be labeled as training data for the ECD task. To automatically label a large number of training data, we need to solve …

Content truncated to 500 words